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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Dec 14, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Seamless integration of image and molecular analysis for spatial transcriptomics workflows.

Joseph Bergenstråhle¹, Ludvig Larsson¹, Joakim Lundeberg²

¹Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65, Solna, Sweden.

|July 16, 2020

Summary

This summary is machine-generated.

We developed STUtility, an R package for processing and visualizing 10x Genomics Visium data. This tool enables 3D mapping of tissue sections, integrating spatial transcriptomics and image data for comprehensive analysis.

Keywords:

3D Data analysis Genomics Image processing R-package Software Spatial transcriptomics Transcriptomics Visualization

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Last Updated: Dec 14, 2025

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Published on: July 6, 2022

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Transcriptomics
Spatial Biology
Bioinformatics

Background:

In situ gene expression technologies are rapidly advancing transcriptomics.
The 10x Genomics Visium platform enables spatial transcriptomics on tissue sections.
Current limitations exist in processing and visualizing 3D spatial transcriptomics data from multiple sections.

Purpose of the Study:

To develop a software solution for processing, aligning, and visualizing 3D spatial transcriptomics data.
To address the need for comprehensive analysis of multiple tissue sections from the 10x Genomics Visium platform.

Main Methods:

Development of an R package named STUtility.
Utilizing the Seurat framework for data analysis.
Standardized data transformation, multi-section alignment, and 3D visualization.

Main Results:

STUtility facilitates standardized processing of 10x Genomics Visium data.
The package enables alignment and integration of multiple tissue sections.
It provides tools for regional annotation and 3D visualization of spatial transcriptomics data.

Conclusions:

STUtility offers a comprehensive solution for analyzing and visualizing spatial transcriptomics data.
The package integrates seamlessly with the Seurat framework, leveraging established analysis methods.
It empowers researchers to create holistic 3D views of tissue architecture and gene expression.